Contrast maps for Cue effects (high_cue > low

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0004.nii'));

spm('Defaults','fMRI')

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0004.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 24759576 bytes Loading image number: 62 Elapsed time is 17.737822 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 5981941 Bit rate: 22.51 bits

contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...

'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...

'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...

'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...

'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...

'motor',...

'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...

'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...

'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...

'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...

'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'

};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 12:36:16 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.97% Expected 3.10 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 40 42 Uncorrected: 6 images Cases 1 17 28 40 42 47 Retained 9 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 53.23% Expected 3.10 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 3 4.8387 global_mean_to_variance 1 1.6129 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.2258 mahal_cov_uncor 6 9.6774 mahal_cov_corrected 2 3.2258 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 6 9.6774 Overall_corrected 2 3.2258

SPM12: spm_check_registration (v7759) 12:36:43 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 62

%for s = 1:length(wh_outlier_corr)

%disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 2 participants, size is now 60

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-0093" "participants that are outliers:... sub-0098"

disp(n);

{'sub-0093'} {'sub-0098'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:36:49 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.015496 Image 1 72 contig. clusters, sizes 1 to 29005 Positive effect: 30527 voxels, min p-value: 0.00000000 Negative effect: 414 voxels, min p-value: 0.00000906

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:36:51 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .001, 'fdr');

Image 1 FDR q < 0.001 threshold is 0.000052 Image 1 65 contig. clusters, sizes 1 to 2320 Positive effect: 5228 voxels, min p-value: 0.00000000 Negative effect: 3 voxels, min p-value: 0.00000906

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:36:52 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 51 voxels displayed, 5180 not displayed on these slices

sagittal montage: 130 voxels displayed, 5101 not displayed on these slices

sagittal montage: 84 voxels displayed, 5147 not displayed on these slices

axial montage: 758 voxels displayed, 4473 not displayed on these slices

axial montage: 741 voxels displayed, 4490 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ______________ __________ ________________ -0.1575 {'depression'} 0.1763 {'movements' } -0.14255 {'affect' } 0.17293 {'rest' } -0.14088 {'reward' } 0.16409 {'lip' } -0.13448 {'disorder' } 0.16254 {'hand' } -0.13312 {'neutral' } 0.15759 {'sensorimotor'} -0.12881 {'outcome' } 0.15566 {'action' } -0.12665 {'anxiety' } 0.15446 {'actions' } -0.12463 {'affective' } 0.13797 {'motor' } -0.12107 {'conflict' } 0.13771 {'gestures' } -0.11963 {'dopamine' } 0.13674 {'execution' }

% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0117 -1.8832 0.0644 0.0000 Cog Wholebrain 0.0118 2.3818 0.0204 1.0000 Emo Wholebrain 0.0005 0.0888 0.9295 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ ________ {'Pain Wholebrain'} -0.01166 0.00619 -1.8837 0.064369 -0.23924 {'Cog Wholebrain' } 0.011769 0.0049426 2.3811 0.020397 0.3024 {'Emo Wholebrain' } 0.00051538 0.0058777 0.087685 0.93041 0.011136

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [9.0001 10.0001 11.0001]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ ___________ _________ ________ ________ _________ {'Pain Wholebrain'} -0.010009 0.0062817 -1.5933 0.11626 -0.20235 {'Cog Wholebrain' } 0.01146 0.0046536 2.4625 0.016635 0.31274 {'Emo Wholebrain' } -0.00095389 0.0058642 -0.16266 0.87132 -0.020658

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [12.0001 13.0001 14.0001]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0005.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0005.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 24759576 bytes Loading image number: 62 Elapsed time is 30.059674 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 5980912 Bit rate: 22.51 bits

contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...

'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...

'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...

'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...

'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...

'motor',...

'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...

'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...

'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...

'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...

'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'

};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 12:38:18 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.97% Expected 3.10 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 4 images Cases 37 40 42 50 Uncorrected: 5 images Cases 28 37 40 42 50 Retained 9 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 53.23% Expected 3.10 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 4 images Outlier_count Percentage _____________ __________ global_mean 2 3.2258 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.2258 mahal_cov_uncor 5 8.0645 mahal_cov_corrected 4 6.4516 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 8.0645 Overall_corrected 4 6.4516

SPM12: spm_check_registration (v7759) 12:38:44 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 62

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 4 participants, size is now 58

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are …" "participants that are …" "participants that are …" "participants that are …"

disp(n);

{'sub-0086'} {'sub-0093'} {'sub-0098'} {'sub-0112'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:38:49 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.005469 Image 1 92 contig. clusters, sizes 1 to 5274 Positive effect: 1025 voxels, min p-value: 0.00000012 Negative effect: 9899 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:38:51 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 101 voxels displayed, 10823 not displayed on these slices

sagittal montage: 161 voxels displayed, 10763 not displayed on these slices

sagittal montage: 87 voxels displayed, 10837 not displayed on these slices

axial montage: 1595 voxels displayed, 9329 not displayed on these slices

axial montage: 1923 voxels displayed, 9001 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _____________ __________ ______________ -0.35722 {'visual' } 0.25102 {'affect' } -0.33564 {'movements'} 0.24648 {'negative' } -0.33071 {'spatial' } 0.24447 {'rating' } -0.32956 {'hand' } 0.23367 {'emotion' } -0.32895 {'motion' } 0.2333 {'regulation'} -0.30359 {'eye' } 0.22605 {'person' } -0.29037 {'position' } 0.22078 {'positive' } -0.2744 {'shape' } 0.21616 {'valence' } -0.27051 {'object' } 0.21249 {'affective' } -0.26079 {'action' } 0.21121 {'neutral' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0039 0.6162 0.5401 0.0000 Cog Wholebrain 0.0012 0.2600 0.7957 0.0000 Emo Wholebrain -0.0047 -0.6838 0.4967 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _______ ________ {'Pain Wholebrain'} 0.0038714 0.0062899 0.61549 0.54052 0.078168 {'Cog Wholebrain' } 0.0011533 0.0044483 0.25926 0.79631 0.032926 {'Emo Wholebrain' } -0.0046942 0.0068794 -0.68236 0.49759 -0.08666

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [9.0002 10.0002 11.0002]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _______ _________ {'Pain Wholebrain'} 0.0030414 0.0062263 0.48848 0.62696 0.062037 {'Cog Wholebrain' } 0.0010565 0.0041062 0.25729 0.79782 0.032675 {'Emo Wholebrain' } -0.0038861 0.00678 -0.57317 0.56863 -0.072793

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [12.0002 13.0002 14.0002]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all; close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0006.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond/1stLevel/sub-0013/con_0006.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 24759576 bytes Loading image number: 62 Elapsed time is 12.557262 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 5976073 Bit rate: 22.51 bits

contrast_name = {'P_VC_cue_high_gt_low', 'V_PC_cue_high_gt_low', 'C_PV_cue_high_gt_low', ...

'P_VC_stimlin_high_gt_low', 'V_PC_stimlin_high_gt_low', 'C_PV_stimlin_high_gt_low',...

'P_VC_stimquad_med_gt_other', 'V_PC_stimquad_med_gt_other', 'C_PV_stimquad_med_gt_other',...

'P_VC_cue_int_stimlin','V_PC_cue_int_stimlin', 'C_PV_cue_int_stimlin',...

'P_VC_cue_int_stimquad','V_PC_cue_int_stimquad','C_PV_cue_int_stimquad',...

'motor',...

'P_simple_cue_high_gt_low', 'V_simple_cue_high_gt_low', 'C_simple_cue_high_gt_low', ...

'P_simple_stimlin_high_gt_low', 'V_simple_stimlin_high_gt_low', 'C_simple_stimlin_high_gt_low',...

'P_simple_stimquad_med_gt_other', 'V_simple_stimquad_med_gt_other', 'C_simple_stimquad_med_gt_other',...

'P_simple_cue_int_stimlin', 'V_simple_cue_int_stimlin', 'C_simple_cue_int_stimlin',...

'P_simple_cue_int_stimquad','V_simple_cue_int_stimquad','C_simple_cue_int_stimquad'

};

m = mean(con_data_obj);

% m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 12:39:53 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 19.35% Expected 3.10 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 40 42 50 Uncorrected: 5 images Cases 17 40 42 47 50 Retained 17 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 43.55% Expected 3.10 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 1 1.6129 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.2258 mahal_cov_uncor 5 8.0645 mahal_cov_corrected 3 4.8387 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 8.0645 Overall_corrected 3 4.8387

SPM12: spm_check_registration (v7759) 12:40:20 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 62

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 3 participants, size is now 59

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:..…" "participants that are outliers:..…" "participants that are outliers:..…"

disp(n);

{'sub-0093'} {'sub-0098'} {'sub-0112'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:40:26 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.010296 Image 1 103 contig. clusters, sizes 1 to 12711 Positive effect: 865 voxels, min p-value: 0.00000215 Negative effect: 19693 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:40:28 - 20/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 510 voxels displayed, 20048 not displayed on these slices

sagittal montage: 566 voxels displayed, 19992 not displayed on these slices

sagittal montage: 489 voxels displayed, 20069 not displayed on these slices

axial montage: 3986 voxels displayed, 16572 not displayed on these slices

axial montage: 4399 voxels displayed, 16159 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ____________________ __________ _______________ -0.24827 {'default' } 0.26089 {'visual' } -0.22811 {'person' } 0.24308 {'eye' } -0.22451 {'autobiographical'} 0.21974 {'shape' } -0.21924 {'story' } 0.19805 {'motion' } -0.21618 {'stories' } 0.18816 {'spatial' } -0.20907 {'selfreferential' } 0.18711 {'orientation'} -0.20768 {'restingstate' } 0.16224 {'target' } -0.20493 {'retrieval' } 0.16182 {'object' } -0.19631 {'memories' } 0.16103 {'adaptation' } -0.19346 {'tom' } 0.16042 {'hand' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0084 1.2632 0.2113 0.0000 Cog Wholebrain -0.0122 -2.5739 0.0125 1.0000 Emo Wholebrain 0.0030 0.4243 0.6728 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ ________ ________ {'Pain Wholebrain'} 0.0084087 0.0066607 1.2624 0.2116 0.16033 {'Cog Wholebrain' } -0.012236 0.0047541 -2.5739 0.012502 -0.32688 {'Emo Wholebrain' } 0.0029794 0.0069939 0.426 0.67161 0.054102

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [9.0004 10.0004 11.0004]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ _________ ________ {'Pain Wholebrain'} 0.0072168 0.006324 1.1412 0.25826 0.14493 {'Cog Wholebrain' } -0.012135 0.004465 -2.7178 0.0085426 -0.34516 {'Emo Wholebrain' } 0.0042502 0.0067222 0.63226 0.52958 0.080297

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {62×3 cell} text_han: {62×3 cell} point_han: {62×3 cell} star_handles: [12.0004 13.0004 14.0004]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

drawnow, snapnow;

% save html

% pubdir = pwd;

% pubfilename = '6cond_cueeffect_contrast.mlx';

%

% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...

% 'format', 'html', 'outputDir', pubdir, ...

% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));

% htmlfile = publish(pubfilename, p);

Contrast maps for Cue effects (high_cue > low_cue)

Pain > (Vicarious & Cognitive)

Linear effect of stimulus intensity (low < med < high)

Pain > VC :: load dataset

Pain > VC :: check data coverage

Pain > VC :: Plot diagnostics, before l2norm

Pain > VC :: run robfit

Pain > VC :: remove outliers based on plot

Pain > VC :: plot diagnostics, after l2norm

Pain > VC :: ttest

Pain > VC :: Neurosynth similarity

Vicarious > (Pain & Cognitive)

Linear effect of stimulus intensity (low < med < high)

Vicarious > PC :: load dataset

Vicarious > PC :: check data coverage

Vicarious > PC :: Plot diagnostics, before l2norm

Vicarious > PC :: run robfit

Vicarious > PC :: remove outliers based on plot

Vicarious > PC :: plot diagnostics, after l2norm

Vicarious > PC :: ttest

Vicarious > PC :: Neurosynth similarity

Cognitive > (Pain & Vicarious)

Linear effect of stimulus intensity (low < med < high)

Cognitive > PV :: load dataset

Cognitive > PV :: check data coverage

Cognitive > PV :: Plot diagnostics, before l2norm

Cognitive > PV :: run robfit

Cognitive > PV :: remove outliers based on plot

Cognitive > PV:: plot diagnostics, after l2norm

Cognitive > PV :: ttest

Cognitive > PV :: Neurosynth similarity